Probability assessment for power system voltage stability margin with renewable energy source using slice sampling

Abstract

Summary With the prevalence of renewable energy source in power system, it is necessary to appraise the voltage stability of the integration system by probabilistic methods. The traditional Markov Chain Monte Carlo (MCMC) simulation could show great calculation precision for the probabilistic assessment, but it is always involved with complicated sampling iterations because of the Gibbs sampling method currently used in MCMC simulation. Instead of Gibbs sampling method, this paper presents the application of slice sampling in MCMC simulation for the voltage stability probabilistic assessment of the power system with renewable source. Firstly, the probabilistic models of renewable source generation are constructed. Then, the sample space of renewable source outputs is obtained by slice sampling, and the samples from the sample space are calculated by power flow. Finally, the voltage stability margin is obtained by the result of the power flow calculation, and the probabilistic assessment of the voltage stability is implemented. Furthermore, the MCMC simulations using Gibbs sampling and slice sampling are compared by Gelman‐Rubin diagnostic and Kullback‐Leibler divergence tests on IEEE 14‐bus system and IEEE 39‐bus system, respectively. The results show that the slice sampling method is simpler and more efficient than Gibbs sampling method in the voltage stability probabilistic assessment.

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“…The probabilistic method based on iterative simulations is one of the common approaches for uncertainty determination for system state space model approximation. The uncertainty modeling by PDFs and iterative methods such as Monte Carlo 51 is the most common method. Hence, the load and PV output power uncertainties are modeled by Figures 14 and 15 using the weibull and normal PDFs, respectively.…”